Are self-driving cars using single frame or multiple frame to make decision?
It varies quite substantially between different self-driving paradigms(rather obviously) but for the most part the vast majority of implementations are using a variety of different reference frames in order to make predictions.

For example, Tesla's Autopilot is being fed many different camera feeds as well as radar and ultrasonic signals that are processed in a variety of temporal contexts.

While, for the most part, all of these programs are very tight-lipped, we can make a variety of assumptions based on the information available and educated assumptions.

As with many large, complex ML/AI systems, there is a large amount of compartmentalization where many different connectionist(or sometimes classic) models are combined(à la youtube recommendation system). Tesla is likely utilizing recurrent and convolutional networks where particular modules(combinations of models) are deciding on specific contexts(temporal or signal-based). These outputs are then most likely fed into an actor network which makes real time decisions.